Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Architectural Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
Volume 56 Issue 1
Jan.  2026
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Article Contents
SU Xianggang, WANG Kun, HUANG Min, HUANG Yuehua, LU Huan, WANG Jiehui, YI Tianqi, DANG Longji, WANG Yuanqing. An Intelligent Dynamic Welding Method of Prefabricated Steel Structures Guided by High-Precision 3D Laser Scanning[J]. INDUSTRIAL CONSTRUCTION, 2026, 56(1): 176-184. doi: 10.3724/j.gyjzG26010509
Citation: SU Xianggang, WANG Kun, HUANG Min, HUANG Yuehua, LU Huan, WANG Jiehui, YI Tianqi, DANG Longji, WANG Yuanqing. An Intelligent Dynamic Welding Method of Prefabricated Steel Structures Guided by High-Precision 3D Laser Scanning[J]. INDUSTRIAL CONSTRUCTION, 2026, 56(1): 176-184. doi: 10.3724/j.gyjzG26010509

An Intelligent Dynamic Welding Method of Prefabricated Steel Structures Guided by High-Precision 3D Laser Scanning

doi: 10.3724/j.gyjzG26010509
  • Received Date: 2026-01-05
    Available Online: 2026-02-26
  • Publish Date: 2026-01-22
  • Prefabricated steel structures have been widely employed due to their easy assembly. However, traditional welding processes suffer from low accuracy, limited automation, and significant dependence on manual operation, which hinder the intelligent and efficient development of prefabricated buildings. To address these challenges, this paper proposes an intelligent dynamic welding method for prefabricated steel reinforced concrete (SRC) structures based on high-precision three-dimensional laser guidance. This method enhances welding accuracy, minimizes manual errors, and enables intelligent control of the welding process. It utilizes high-precision 3D laser scanning to capture the spatial characteristics of the welding area, automatically identifying weld positions and shapes through point cloud data processing and intelligent recognition algorithms. The system then precisely plans the welding trajectory. By integrating robotic vision guidance and adaptive control algorithms, it dynamically adjusts welding parameters in real time to accommodate variations in weld gaps, component tolerances, and environmental conditions, thereby improving welding accuracy and consistency. Additionally, a deep learning model is incorporated to intelligently monitor and detect welding defects, enabling real-time evaluation and optimization through weld formation quality analysis and defect recognition. To validate the proposed method, a high-precision 3D laser scanning and intelligent welding experimental platform was developed, and welding accuracy, efficiency, and quality were tested under various conditions. Experimental results demonstrated that this method significantly improved weld positioning accuracy, reduced deformation, enhanced welding strength and consistency, and lowered both construction costs and manual intervention. Compared to traditional welding techniques, the proposed method offers greater intelligence and superior engineering applicability, providing an efficient and reliable welding solution for the intelligent construction of prefabricated SRC structures.
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